Roadmap

[SONAIII]

This is Trood's CLI official roadmap.

Contributor Guide

Trood CLI is in active development, and we need contributors to help shape its architecture and features–check out #22. If you’re interested, join our community in the #trood-cli channel on Discord.

Phase 1: MVP - API-Based CLI 🛠️

• CLI Implementation: Establish a solid command structure and parsing system. 🖥️
• Develop Scanner Module: Implement project file scanning to detect potential issues. 📂
• Implement Log Parser Module: Extract and categorize errors from logs and stack traces. 📝
• Integrate AI Debugging (LLM API): Utilize external LLM APIs to analyze project files and generate debugging suggestions. 🤖
• Develop Solution Finder: Fetch solutions from Stack Overflow, GitHub Issues, and official documentation. 🌐
• Format CLI Output for Readability: Structure and present results in a user-friendly way. 🎨
• Support for Python & JavaScript: Initial language support for debugging. 🔧

✅ Deliverable: A functional CLI tool that scans for issues, explains them via AI, and provides solutions using external sources.

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Phase 2: Enhanced Debugging & Contributor Integration 🚀

• Integrate Talent Hub (TTH) for Expert Discovery: AI suggests relevant contacts from TTH for additional support. 🏢
• Implement AI-Powered Interactive Debugging: Step-by-step guidance for resolving issues interactively. 🧩
• Expand Language & Framework Support: Add support for Go, Java, Rust, and additional popular frameworks. 🌍
• Introduce AI-Generated Fix Descriptions: AI-generated explanations for debugging logs and documentation. 📜
• Implement Basic Auto-Fix Feature (Read-Only): AI suggests and proposes changes but does not modify files yet. 🛠️
• User Feedback Integration: Allow users to rate suggestions and report incorrect fixes. 🗣️

✅ Deliverable: CLI that integrates expert recommendations, interactive debugging, and expands language support.

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Phase 3: Full AI-Autofix & Repository Playground 🎯

• Implement Full Auto-Fix Feature: AI modifies necessary files and creates pull requests to fix detected errors. ✅
• Implement Advanced Talent Hub Integration: AI writes and submits fix requests directly to the TTH platform. 📬
• Create AI-Generated Repository Playground:
  • AI creates isolated bug reproduction environments for debugging.
  • Only extracts necessary files to ensure confidentiality. 🏗️
• Allow Users to Choose Alternative LLM Models: Support for self-hosted or external AI models (e.g., LLaMA, Mistral). 🧠
• Improve Performance & AI Speed: Optimize API interactions for faster response times and caching mechanisms. ⚡

✅ Deliverable: The CLI can auto-fix issues, create pull requests, generate bug reproduction repositories, and integrate fully with TTH.

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Phase 4: Ecosystem Integration & Community Expansion 🔌

• Develop IDE Plugin Prototypes: Create VSCode and IntelliJ extensions for real-time debugging insights. 🖥️
• Develop API for Third-Party Integrations: Provide an extension API so developers can build their own features. 🔗
• Implement CLI Cloud Mode: Store user logs and debugging history for cloud-based analysis & sharing (optional). ☁️
• Enhance Documentation & Contribution Guidelines: Expand detailed guides for community contributions. 📚

✅ Deliverable: The tool expands into IDE support, cloud capabilities, and an extension-friendly API.